Improved Forecasting of Short Term Electricity Demand by using of Integrated Data Preparation and Input Selection Methods

The main aim of this paper is to emphasize on the significant role of data pre-processing phase in improving the short-term load demand forecasting. Different transformation approaches including normalization, Zscore and Box-Cox methods are applied and various input selection methods including forward selection, backward selection, stepwise regression and principle component analysis are used to see how the combination of these preprocessing techniques will influence the performance of different parametric (ARIMA, ARIMAX, MLR) and non-parametric (NAR, NARX, SVR, ANFIS) predictors. The data has been collected from the daily load demand of Ottawa, Canada. It has been observed that the Box-Cox transformation significantly improved the performance of all predictors and the findings have demonstrated the superior role of exogenous variables in accuracy improvement of all predictors. In terms of MAPE, the value of 2.27% for ARIMA model improved to 1.75% with ARIMAX using temperature, and it decreased from 1.46% to 1.334% by means of NARX model using normalized PCA which is applied to normalized data. In an overall view, the non-parametric algorithms have had a considerable gain over parametric models and NARX network has the highest accuracy among all of the predictors.